What is Stochastic Gradient Descent?

What is Stochastic Gradient Descent?

Stochastic Gradient Descent (SGD) is an optimization algorithm used to minimize a function by iteratively updating parameters.

How does it differ from regular gradient descent?

Unlike regular gradient descent, which uses the entire dataset to compute gradients, SGD uses a single sample or a small batch, making it faster.

What are the advantages of using SGD?

SGD can converge faster and is more efficient for large datasets. It also helps to escape local minima due to its noisy updates.

Are there any disadvantages?

Yes, SGD can be less stable and may require careful tuning of the learning rate to ensure convergence.

Can you give an example of where SGD is used?

SGD is commonly used in training deep learning models, such as neural networks, where large datasets are involved.